Abstract
The decrease of sequencing cost in the recent years has made genome-wide studies of transcription factor (TF) binding through chromatin immunoprecipitation methods like ChIP-seq and chromatin immunoprecipitation with lambda exonuclease (ChIP-exo) more accessible to a broader group of users. Especially with ChIP-exo, it is now possible to map TF binding sites in more detail and with less noise than previously possible. These improvements came at the cost of making the analysis of the data more challenging, which is further complicated by the fact that to this date no complete pipeline is publicly available. Here we present a workflow developed specifically for ChIP-exo data and demonstrate its capabilities for data analysis. The pipeline, which is completely publicly available on GitHub, includes all necessary analytical steps to obtain a high confidence list of TF targets starting from raw sequencing reads. During the pipeline development, we emphasized the inclusion of different quality control measurements and we show how to use these so users can have confidence in their obtained results.
Highlights
Transcription factors (TFs) are one of the main determinants of transcriptional regulation and there has been much interest in precisely mapping their binding sites to identify their regulatory targets
In 2011, this method was further refined by Rhee et al using an additional exonuclease treatment following the immunoprecipitation, to improve the resolution of the mapped TF peaks to the single nucleotide level and to increase the signal-to-noise ratio (SNR) of the method [3]
We show the results from the analysis pipeline run on chromatin immunoprecipitation (ChIP)-exo sequencing data of Ino2 in two different conditions; respiratory glucose metabolism using glucose limitation and gluconeogenic respiration using ethanol limitation
Summary
Transcription factors (TFs) are one of the main determinants of transcriptional regulation and there has been much interest in precisely mapping their binding sites to identify their regulatory targets To this date, several methods have been developed to map TF binding sites in the genome. For analyzing the data is publicly available We demonstrate such a pipeline, starting from the raw sequencing files, to identified peaks and gene targets. This pipeline stitches together heavily used software tools from other parts of the genomic research areas, like Bowtie for mapping the sequencing reads [5], and a peak finding tool developed for ChIP-seq and ChIP-exo called Genome wide Event finding and Motif discovery (GEM) [6]. The pipeline includes analytical steps leveraging the high resolution of ChIP-exo which would not be possible with ChIP-seq data
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